Empathy, Customer Discovery and Design Research

Introduction

Entrepreneurial innovation begins with understanding people before attempting to design for them. Many ventures fail not because their technologies are poor, but because they solve problems that customers do not experience, do not value, or cannot realistically adopt. Empathy, customer discovery and design research provide systematic ways to investigate these problems.

Empathy in design does not mean simply feeling sorry for users or assuming that the researcher understands their experience. It means developing an informed understanding of people’s goals, constraints, emotions, practices and social contexts. Customer discovery is the disciplined process of testing assumptions about customers, their problems, existing alternatives and willingness to change. Design research supplies the methods through which this understanding is developed, including observation, interviews, ethnography, surveys and contextual inquiry.

This chapter treats research as both an analytical and ethical activity. Researchers must gather useful evidence while respecting privacy, autonomy, dignity and cultural difference. They must also recognize that every method produces a partial representation of reality. A survey may reveal patterns but conceal meaning; an interview may reveal meaning but be influenced by memory and social desirability; observation may expose actual behavior but miss private motives. Effective entrepreneurial research therefore combines methods and examines contradictions rather than forcing premature certainty.

Learning Objectives

By the end of this chapter, students should be able to:

1. Explain the role of empathy and customer discovery in entrepreneurial innovation.
2. Select and plan appropriate qualitative and quantitative research methods.
3. Conduct ethical observations, interviews, ethnographic studies, surveys and contextual inquiries.
4. Identify sampling, measurement, interpretation and researcher biases.
5. Synthesize evidence into empathy maps, personas, customer journeys and jobs-to-be-done.
6. Distinguish stated preferences from actual behavior and unmet needs.
7. Translate research findings into defensible opportunity hypotheses.

1. From Assumptions to Evidence

An early venture contains many assumptions: who the customer is, what problem matters, how often it occurs, what people currently do, who makes the purchase decision, what price is acceptable and what barriers prevent adoption. These assumptions should be made explicit before research begins.

A useful assumption statement has four elements:

We believe that [customer segment] experiences [problem] in [context], currently uses [alternative], and would value [desired outcome].

For example: “We believe that independent food vendors lose sales because they cannot predict daily demand, currently rely on informal experience, and would value a simple forecasting service.” This statement is testable. It does not yet claim that the solution is correct.

Customer discovery should normally proceed from the problem toward the solution. Asking potential users whether they like a proposed product often generates politeness rather than valid evidence. Better questions investigate recent behavior: What happened the last time the problem occurred? What did the person do? What did it cost in time, money, stress or opportunity? Who else was involved? What alternatives were considered?

Desirability, Feasibility and Viability

Design decisions are often evaluated through three interacting dimensions:

• Desirability: Do people experience a meaningful problem and want a better outcome?
• Feasibility: Can the organization build, deliver and support the proposed offering?
• Viability: Can the offering create sustainable economic value?

Design research primarily informs desirability, but findings may also reveal feasibility and viability constraints. For example, interviews with hospital staff may show strong demand for a digital service, while observation reveals that the service would need to operate during network outages and comply with strict access controls.

2. Empathy as a Research Discipline

Empathy has cognitive, emotional and practical dimensions. Cognitive empathy involves understanding how people interpret a situation. Affective empathy concerns sensitivity to their feelings. Behavioral or compassionate empathy involves using that understanding to act appropriately. In professional design research, empathy must be supported by evidence. Researchers should not substitute imagination for inquiry.

Empathy also requires recognizing the difference between the researcher’s perspective and the participant’s lived experience. A student designing a service for older adults may understand the general concept of ageing but still misunderstand mobility, stigma, household support or technology confidence. The participant’s experience is not a problem to be “decoded” by an expert alone; it is a source of knowledge that should be represented accurately and respectfully.

A disciplined empathy process involves:

1. Entering the participant’s context: observing real settings and routines.
2. Listening without premature evaluation: allowing participants to describe experiences in their own terms.
3. Probing for concrete examples: asking about recent events rather than general opinions.
4. Comparing accounts with behavior: looking for convergence and contradiction.
5. Making interpretations visible: separating direct evidence from researcher inference.
6. Returning to participants where appropriate: checking whether interpretations are reasonable.

Empathy is not equivalent to agreeing with participants, adopting every request or abandoning professional judgment. It is a foundation for informed judgment.

3. Planning Ethical User Research

A research plan should specify the decision the research will inform, the questions to be answered, the participants required, the method, the recruitment process, the analysis approach and the ethical safeguards.

3.1 Research Questions

Good research questions are open, specific and connected to a decision. Examples include:

• How do first-year students currently find affordable textbooks?
• What workarounds do small retailers use when inventory data are incomplete?
• What prevents caregivers from using existing appointment systems?

Weak questions include “Would you use our app?” and “Do you like our idea?” These questions invite hypothetical answers and rarely uncover behavior, context or trade-offs.

3.2 Participants and Sampling

A population is the broader group about which the research is concerned. A sample is the subset actually studied. In exploratory qualitative research, participants are usually selected purposively because they possess relevant experience. Researchers may seek maximum variation, such as differences in age, location, income, expertise or frequency of use.

Important participant categories may include:

• primary users, who directly use the product or service;
• purchasers, who pay for it;
• beneficiaries, who receive the value;
• decision-makers, who authorize adoption;
• influencers, who affect the decision; and
• affected non-users, who experience consequences without using it.

A narrow focus on the purchaser can miss the person who performs the work or bears the risk. In business-to-business settings, the user, buyer, administrator and beneficiary may be different people.

Qualitative sample adequacy is not determined by a universal number. Researchers continue until additional data produce little new insight in relation to the research question, a condition often called thematic saturation. Saturation should not be treated mechanically: a diverse population may require separate attention to important subgroups.

3.3 Informed Consent and Participant Welfare

Ethical research requires that participants understand, in accessible language:

• the purpose of the study;
• what participation involves;
• foreseeable risks and benefits;
• whether participation is voluntary;
• how data will be recorded, stored and used;
• whether quotations or images may be published; and
• how they may withdraw, where withdrawal is practicable.

Consent is a process, not merely a signature. Participants should not be pressured by employers, teachers, service providers or researchers who control valued resources. Incentives should compensate time without becoming coercive. Extra safeguards are needed when working with children, people with impaired decision-making capacity, vulnerable communities or sensitive topics.

Privacy and confidentiality are different. Privacy concerns a person’s control over access to themselves and their information. Confidentiality concerns the researcher’s duty to protect information entrusted to them. Anonymization reduces identifying information, but small communities or distinctive quotations may still make a participant recognizable.

Researchers should collect only data relevant to the purpose, restrict access, establish retention periods and avoid recording sensitive information unnecessarily. Photographs, audio, video, location data and digital traces require explicit consideration because they can identify people even when names are removed.

4. Core Design Research Methods

4.1 Observation

Observation studies what people do in a defined setting. It can be structured, using a predesigned checklist, or unstructured, allowing relevant practices to emerge. It can be participant observation, where the researcher takes part in an activity, or non-participant observation, where the researcher remains outside the activity.

A useful observation protocol records:

• setting and time;
• actors and roles;
• sequence of activities;
• tools, objects and information flows;
• interruptions and workarounds;
• interactions among people;
• environmental constraints; and
• notable quotations or events.

Researchers should distinguish descriptive notes from interpretive notes. “The worker checked three paper forms before entering data” is descriptive. “The system is frustrating” is interpretive unless supported by observed evidence or participant comment. This distinction improves analytical transparency.

Observation is especially valuable when people cannot easily articulate tacit routines. However, the presence of a researcher may change behavior, and short observations can overrepresent unusual events. Repeated visits and unobtrusive procedures can reduce these limitations.

4.2 Semi-Structured Interviews

A semi-structured interview combines consistent topic coverage with flexibility. An interview guide might include opening questions, behavioral questions, probes and closing questions.

Examples of effective prompts include:

• “Tell me about the last time this happened.”
• “What did you do next?”
• “What made that difficult?”
• “What alternatives did you consider?”
• “Who else was affected?”
• “What would success have looked like?”

Questions beginning with “why” can sound accusatory. “What led you to choose that approach?” is often more productive. Researchers should avoid leading questions, double-barrelled questions and jargon.

Interviews reveal meanings, motivations and interpretations, but they are not direct measurements of behavior. Participants may forget, rationalize, exaggerate, protect their self-image or provide answers they believe are socially acceptable. Recent concrete episodes are generally more reliable than broad predictions about future use.

4.3 Ethnography

Ethnography is a contextual approach to studying culture, practices, relationships and meaning. Classical ethnography may involve prolonged immersion, whereas entrepreneurial projects often use focused or rapid ethnography. Even short studies can be ethnographically informed when they examine routines, norms, language, status, material objects and institutional structures rather than treating behavior as isolated.

An ethnographic researcher asks:

• What is considered normal in this setting?
• Which rules are formal and which are informal?
• Who has authority and who performs invisible labor?
• What meanings do objects and practices carry?
• How do people adapt when resources are limited?

Ethnography can reveal opportunities that users do not state directly. A commuter may say that ticket purchasing is simple, while observation shows that the process depends on borrowing another person’s phone, navigating unreliable connectivity and asking a staff member for help.

Ethnographic interpretation demands reflexivity. Researchers should examine how their identity, institutional position, language and expectations affect access and interpretation. Field notes should record not only participants but also the researcher’s decisions and reactions.

4.4 Contextual Inquiry

Contextual inquiry combines observation and interviewing in the setting where work occurs. The participant demonstrates an activity while the researcher asks clarifying questions. Four principles are commonly associated with the method:

• Context: study behavior where it naturally occurs.
• Partnership: treat the participant as an expert on their work.
• Interpretation: develop and check meaning collaboratively.
• Focus: maintain a research focus while remaining open to discovery.

For example, instead of asking a warehouse employee to describe stock reconciliation in a meeting room, the researcher observes the process at the warehouse, asks what each document means, and investigates exceptions as they occur. Contextual inquiry is particularly useful for complex workflows and tacit knowledge.

4.5 Surveys

Surveys are useful for describing patterns, comparing groups and estimating the prevalence of reported attitudes or behaviors. They are less suitable for discovering unknown problems in depth. A survey should follow, rather than replace, exploratory work when the problem space is not yet understood.

A sound survey requires clear constructs, appropriate response options, neutral wording and a sampling strategy. Researchers should distinguish:

• open-ended items, which allow unanticipated answers;
• nominal items, which classify categories without order;
• ordinal items, which express ordered categories; and
• scale items, which measure an attitude or perception using several response levels.

A leading item such as “How valuable would our convenient platform be?” embeds a positive judgment. A more neutral alternative is “Which of the following methods do you currently use?”

A sample can be large yet misleading if it is self-selected, excludes important groups or has a low response rate. Survey findings should therefore report who responded, how they were recruited, when the data were collected and what limitations apply.

5. Bias, Validity and Research Quality

Bias is a systematic influence that produces a distorted representation of a phenomenon. It can enter at any stage.

Validity concerns whether a method supports the interpretation being made. Reliability concerns consistency of measurement or procedure. Qualitative research may use related standards such as credibility, dependability, confirmability and transferability. No method is automatically valid; validity depends on the question and the inference.

Triangulation strengthens research by comparing different sources, methods, researchers or theoretical perspectives. For instance, interview claims about delays can be compared with direct observation and service records. Triangulation does not require all sources to agree. Disagreement may reveal different experiences or hidden conditions.

6. Analysis and Synthesis

Research produces raw data: recordings, transcripts, field notes, photographs, survey responses and artifacts. These do not become insight automatically. Analysis involves organizing evidence, coding meaningful segments, comparing cases and developing interpretations.

A practical qualitative process is:

1. Familiarize: read or review the complete material.
2. Code: label relevant actions, needs, barriers, emotions and workarounds.
3. Compare: examine similarities, differences and exceptions.
4. Cluster: group related codes into themes.
5. Interpret: connect themes to the research question.
6. Test: search for contradictory evidence and alternative explanations.
7. Synthesize: express findings as evidence-based opportunity statements.

A code should describe evidence rather than merely repeat a desired solution. “Needs mobile app” is usually a solution code. “Cannot access records while away from desk” describes a need or context and leaves several solutions open.

Empathy Maps

An empathy map organizes evidence around what a person says, thinks, does and feels. It may also include pains, gains, influences and barriers. Researchers should label whether an entry is a direct quote, an observed behavior or an inference. An empathy map is a synthesis device, not a psychological diagnosis.

Personas

A persona is a research-based representation of a meaningful user pattern. It typically includes goals, behaviors, context, frustrations, capabilities and needs. A persona should not be a fictional stereotype such as “Sarah, 24, likes technology.” Demographic details are useful only when they explain behavior or access.

Personas should be:

• grounded in multiple evidence sources;
• specific enough to guide decisions;
• distinguished from other relevant patterns; and
• treated as provisional and revisable.

A persona must not erase diversity within a group. Teams should state its evidence base and avoid using personas to make unsupported claims about an entire population.

Customer Journey Maps

A customer journey map represents stages of an experience, such as awareness, search, purchase, use, support and renewal. It can include customer actions, goals, touchpoints, emotions, pain points, backstage processes and opportunities.

Journey mapping reveals that an experience is a system rather than a single interface. A customer may be satisfied during use but frustrated by onboarding, payment or complaint resolution. The journey should be based on actual evidence and should identify whose journey is being mapped.

Jobs-to-be-Done

The jobs-to-be-done perspective focuses on the progress a person is trying to make in a particular situation. A job is not simply a product feature or demographic category. A useful job statement identifies context, motivation and desired progress:

When [situation], I want to [motivation], so I can [desired outcome].

For example: “When preparing a class presentation with incomplete information, I want to quickly assess which sources are trustworthy, so I can contribute confidently without spending the entire evening verifying them.”

Jobs may be functional, emotional or social. They help researchers distinguish the underlying outcome from a current solution. However, jobs-to-be-done should not be used as a universal replacement for cultural, institutional or power analysis. The same job can have different meanings and constraints across communities.

7. From Findings to Unmet Needs

An unmet need is not simply something a participant requests. It is a meaningful gap between a desired outcome and the current experience. Evidence of an unmet need may include repeated workarounds, abandonment, delay, anxiety, excessive cost, dependence on informal help or acceptance of poor alternatives.

A useful opportunity statement has the form:

[User or group] needs a way to [desired progress] because [evidence-based barrier or insight].

The statement should avoid specifying the solution too early. “Students need a way to compare textbook costs because they discover price differences only after investing time in course preparation” leaves room for several possible interventions.

Researchers should rank opportunities using criteria such as frequency, severity, strategic fit, affected population, accessibility and feasibility. Frequency alone can mislead: a rare problem may be extremely consequential, while a frequent irritation may not justify behavior change.

8. Illustrative Case: FreshRoute for Independent Food Vendors

Background

FreshRoute is a fictional early-stage venture exploring a service that could help independent food vendors plan daily ingredient purchases. The founders initially assumed that vendors needed a sophisticated forecasting application.

Problem and Research Plan

The team reframed its initial assumption: vendors may struggle not with forecasting in the abstract, but with making purchasing decisions under uncertainty. It planned research with 12 vendors across three markets, combining two visits to each market, semi-structured interviews, contextual inquiry during opening preparation and a short survey about purchasing frequency and waste. Participation was voluntary, and the team obtained permission before photographing work areas. No financial records were copied without consent.

Analysis

Interviews suggested that vendors wanted to reduce waste. Observation revealed a more complex situation. Vendors adjusted purchases based on weather, school schedules, nearby events, supplier reliability and informal information from neighboring sellers. Several used notebooks, but the main difficulty was not arithmetic. It was that supplier minimum orders and late deliveries forced them to make imperfect decisions. One vendor relied on a family member to check prices by phone; another deliberately overbought one ingredient because running out would damage reputation.

The team created a journey map showing forecasting, supplier contact, purchasing, preparation, selling and end-of-day disposal. The greatest pain point occurred before purchasing, when information was incomplete and decisions were time-sensitive. The team also created two provisional personas: a high-volume vendor managing multiple employees and a solo vendor whose time and cash flow were tightly constrained.

A jobs-to-be-done statement emerged: “When deciding what to buy before a variable sales day, I want to combine local signals and supplier constraints quickly, so I can protect cash flow without disappointing customers.”

Approach and Outcome

Rather than immediately building a forecasting application, the team tested a low-technology service: a daily message summarizing local event information, weather conditions and supplier availability, combined with a simple purchasing checklist. Vendors evaluated the usefulness after actual purchasing decisions. The trial showed interest in timely local information, but also exposed concerns about inaccurate forecasts and the burden of entering data.

The resulting opportunity was narrower and more credible: support rapid purchasing decisions with transparent signals and adjustable recommendations, rather than promise precise demand prediction. Further research was required before selecting a business model.

Learning Points

1. Stated demand for “forecasting” concealed a broader decision problem.
2. Observation exposed social and operational factors absent from interviews.
3. Existing workarounds indicated both unmet needs and valued practices.
4. A low-fidelity test reduced the cost of being wrong.
5. Research changed the solution concept rather than merely validating it.

9. Technology, AI and Contemporary Practice

Digital tools can assist transcription, tagging, translation, survey distribution and pattern exploration. Generative AI may help summarize large volumes of text, but it can omit minority views, invent themes, reproduce stereotypes or expose confidential data. Researchers remain responsible for checking outputs against source material.

AI-generated transcripts require verification, especially for accents, technical terms and code-switching. Sensitive recordings should not be uploaded to a service unless data governance, consent, security and contractual conditions are understood. Participants should be informed when automated processing materially affects the research.

Remote research expands access but may exclude people with limited connectivity, disabilities, low digital literacy or unsafe home environments. Digital analytics also raise questions about consent, secondary use, surveillance and ownership. A technically available data source is not automatically ethically legitimate to use.

Contemporary design research increasingly addresses accessibility, climate impact, inclusive design, decolonial methods and participatory practice. Inclusive research involves people affected by a design decision not merely as subjects, but where appropriate as partners in framing questions, interpreting evidence and deciding outcomes. Participation must be meaningful rather than symbolic.

10. Best-Practice Research Workflow

A practical workflow for entrepreneurial research is:

1. State the decision and assumptions.
2. Formulate research questions rather than solution questions.
3. Identify all relevant actors and affected groups.
4. Select complementary methods.
5. Prepare consent, privacy and data-management procedures.
6. Pilot the guide or instrument.
7. Collect behavioral and contextual evidence.
8. Record descriptive and interpretive notes separately.
9. Analyze systematically and search for disconfirming evidence.
10. Synthesize personas, journeys or jobs only when supported by evidence.
11. Share findings with limitations and uncertainty.
12. Convert findings into testable opportunity hypotheses.
13. Return to research after prototyping and learning.

The goal is not to eliminate uncertainty. Entrepreneurial research makes uncertainty visible, reduces avoidable errors and improves the quality of decisions.

Challenges and Limitations

Design research is constrained by time, access, budget and participant availability. Research findings may not generalize statistically, particularly when samples are purposive. Organizations may also misuse personas as fixed market segments or treat a memorable quotation as representative evidence.

Researchers must resist the pressure to produce positive results. Participants may fear criticizing a founder, and teams may selectively report evidence to secure funding. Transparent documentation of methods, contradictions and limitations is therefore a professional responsibility.

There is also a power issue: researchers often decide which voices count and how findings are translated into strategy. Ethical practice requires asking who benefits, who bears risk and who is absent. A product can be desirable to a paying customer while harming workers, communities or the environment. Customer discovery should therefore be connected to stakeholder analysis and responsible innovation.

Chapter Summary

Empathy, customer discovery and design research help entrepreneurs understand people’s lived experiences before committing to solutions. Effective research begins with explicit assumptions and decision-focused questions. Observation, interviews, ethnography, contextual inquiry and surveys produce different kinds of evidence and should be selected according to the research purpose.

Ethical practice requires informed consent, voluntary participation, privacy protection, careful data governance and respect for vulnerable groups. Bias can enter through sampling, recruitment, questioning, observation, memory and interpretation. Triangulation, reflexivity and attention to contradictory evidence improve research quality.

Empathy maps, personas, customer journey maps and jobs-to-be-done are synthesis tools. They are valuable only when grounded in evidence and treated as provisional representations. Unmet needs are discovered by examining gaps between desired outcomes and current experiences, especially through workarounds, delays, risks and trade-offs. The entrepreneurial objective is not to prove an idea correct, but to learn whether a meaningful and responsibly addressable opportunity exists.

Key Terms

Empathy: Evidence-informed understanding of another person’s experience, perspective and context.

Customer discovery: Systematic investigation of customers, problems, alternatives and adoption conditions.

Design research: Research conducted to understand people, contexts and opportunities for design.

Observation: Study of behavior and interaction in a defined setting.

Semi-structured interview: Interview using planned topics with flexibility for probing and follow-up.

Ethnography: Contextual study of culture, practices, meanings and relationships.

Contextual inquiry: Observation and interviewing conducted while a participant performs real work.

Survey: Structured instrument used to collect comparable responses from participants.

Population: Broader group to which a research question refers.

Sample: Subset of a population that is actually studied.

Purposive sampling: Selection of participants because they possess relevant characteristics or experience.

Thematic saturation: Point at which additional qualitative data produce little new insight for the research purpose.

Informed consent: Voluntary agreement based on understandable information about participation.

Confidentiality: Duty to protect information provided by participants.

Triangulation: Comparison of evidence from different methods, sources or researchers.

Persona: Evidence-based representation of a meaningful user pattern.

Customer journey map: Representation of stages, touchpoints, emotions and processes in an experience.

Jobs-to-be-done: Perspective focused on the progress a person seeks in a particular situation.

Unmet need: Meaningful gap between a desired outcome and the current experience.

Researcher reflexivity: Critical examination of how the researcher influences research access, interpretation and outcomes.

Review Questions

Short Answer Questions

1. Why should customer discovery usually begin with problems and behaviors rather than solution preferences?
2. Distinguish privacy from confidentiality in user research.
3. What is the purpose of contextual inquiry?
4. Why should descriptive and interpretive field notes be separated?
5. Define a jobs-to-be-done statement.

Long Answer Questions

1. Compare observation, semi-structured interviews, ethnography, contextual inquiry and surveys. Explain the type of evidence each method produces and its main limitations.
2. Explain how sampling, recruitment, questioning and interpretation can introduce bias into entrepreneurial research.
3. Describe an ethical research plan for investigating a digital service used by a vulnerable population.
4. Explain how empathy maps, personas and customer journey maps support design decisions while also creating risks of oversimplification.
5. Discuss why triangulation and reflexivity are important for credible design research.

Analytical / Application Questions

1. A university believes students need a new food-ordering application. Design a research plan that tests the underlying problem without leading participants toward an app-based solution.
2. Interviewees say that a business process is easy, but observation shows repeated workarounds and delays. Analyze possible explanations and propose additional research.
3. Develop a provisional persona, journey-map structure and jobs-to-be-done statement for a chosen entrepreneurial opportunity. Identify the evidence still needed before using them for a product decision.

Multiple Choice Questions with Answers

Question 1

Which research question is most suitable for customer discovery?

A. Would you buy our proposed application?

B. Do you like the colors in our prototype?

C. Tell me about the last time you experienced this problem and what you did.

D. Would a cheaper version solve everything?

Correct Answer: C. Tell me about the last time you experienced this problem and what you did.

Question 2

What is the primary purpose of contextual inquiry?

A. To study behavior only through an online questionnaire

B. To observe and discuss work while it occurs in its real setting

C. To test statistical significance in a laboratory

D. To create a fictional demographic profile

Correct Answer: B. To observe and discuss work while it occurs in its real setting

Question 3

Which statement best distinguishes descriptive from interpretive field notes?

A. Descriptive notes record observed events, while interpretive notes record possible meanings

B. Descriptive notes contain opinions, while interpretive notes contain only quotations

C. Descriptive notes are quantitative, while interpretive notes are always qualitative

D. There is no meaningful distinction between them

Correct Answer: A. Descriptive notes record observed events, while interpretive notes record possible meanings

Question 4

A researcher recruits only friends who already support the proposed venture. What is the most direct concern?

A. Recall bias

B. Sampling and recruitment bias

C. Instrument reliability

D. Thematic saturation

Correct Answer: B. Sampling and recruitment bias

Question 5

Which is the strongest example of an evidence-based persona characteristic?

A. Alex is 27 and likes modern technology

B. Alex represents every customer in the market

C. Alex frequently relies on a colleague because the service requires assistance during busy periods

D. Alex will definitely purchase the premium plan

Correct Answer: C. Alex frequently relies on a colleague because the service requires assistance during busy periods

Question 6

What does triangulation involve?

A. Repeating one interview question three times

B. Comparing evidence from different methods, sources or researchers

C. Selecting exactly three participants

D. Removing contradictory evidence

Correct Answer: B. Comparing evidence from different methods, sources or researchers

Question 7

Which statement best describes an unmet need?

A. Any feature requested by a participant

B. A gap between a desired outcome and the current experience

C. A demographic category used for advertising

D. A solution that has already been commercially validated

Correct Answer: B. A gap between a desired outcome and the current experience

Question 8

Which interview prompt is most likely to produce concrete behavioral evidence?

A. Would you use our product in the future?

B. Do you agree that the current process is inefficient?

C. Tell me about the last time you completed this task.

D. How much would you pay for our idea?

Correct Answer: C. Tell me about the last time you completed this task.

Question 9

Why can a large survey sample still produce misleading conclusions?

A. Large samples cannot contain quantitative information

B. A large sample may still exclude important groups or be self-selected

C. Surveys automatically eliminate social desirability bias

D. Survey responses cannot be compared

Correct Answer: B. A large sample may still exclude important groups or be self-selected

Question 10

Which jobs-to-be-done statement is best formed?

A. Users need an artificial-intelligence dashboard

B. When preparing under time pressure, I want to assess source reliability quickly so I can contribute confidently

C. Students aged 20 to 25 prefer innovation

D. The product should include five screens

Correct Answer: B. When preparing under time pressure, I want to assess source reliability quickly so I can contribute confidently